In vivo optical flow imaging

ABSTRACT

Amplitude decorrelation measurement is sensitive to transverse flow and immune to phase noise in comparison to Doppler and other phase-based approaches. However, the high axial resolution of OCT makes it very sensitive to the pulsatile bulk motion noise in the axial direction, resulting in unacceptable signal to noise ratio (SNR). To overcome this limitation, a novel OCT angiography technique based on the decorrelation of OCT signal amplitude due to flow was created. The full OCT spectrum can be split into several narrower spectral bands, resulting in the OCT resolution cell in each band being isotropic and less susceptible to axial motion noise. Inter-B-scan decorrelation can be determined using the narrower spectral bands separately and then averaged. Recombining the decorrelation images from the spectral bands yields angiograms that use the full information in the entire OCT spectral range. Such images showed significant improvement of SNR for both flow detection and connectivity of microvascular networks when compared to other amplitude-decorrelation techniques. Further, creation of isotropic resolution cells can be useful for quantifying flow having equal sensitivity to axial and transverse flow. Such improved non-invasive imagery can be useful in the diagnosis and management of a variety of diseases.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional PatentApplication No. 61/594,967 filed Feb. 3, 2012, entitled “In Vivo OpticalFlow Imaging,” the entire disclosure of which is hereby incorporated byreference in its entirety.

ACKNOWLEDGMENT OF GOVERNMENT SUPPORT

This invention was made with government support under grant numbersR01-EY013516 awarded by the National Institutes of Health. Thegovernment has certain rights in the technology.

FIELD

This disclosure relates generally to the field of biomedical imaging,and more specifically to methods, apparatuses, and systems associatedwith optical coherence tomography and angiography.

BACKGROUND

In vivo three-dimensional mapping of biologic tissue and vasculature isa challenging proposition due to the highly-scattering and absorptivenature of biologic tissue. Some current methods have slow scanningspeeds making in vivo three-dimensional imaging difficult. Some othertechniques having faster scanning speeds are still lacking due to theirinability to scan deeply into biologic tissue without producingoverlapped images, requiring the use of invasive procedures to scan thetissue of interest. Many techniques aimed at deeper imaging generallycannot provide deep imaging of tissue having moving material (e.g.,blood flow). Therefore, methods to effectively image structure and/ortissue movement, such as blood flow, are of substantial clinicalimportance.

Optical coherence tomography (OCT) is an imaging modality forhigh-resolution, depth-resolved cross-sectional, and 3-dimensional (3D)imaging of biological tissue. Among its many applications, ocularimaging in particular has found widespread clinical use. In the lastdecade, due to the development of light source and detection techniques,Fourier-domain OCT, including spectral (spectrometer-based) OCT andswept-source OCT, have demonstrated superior performance in terms ofsensitivity and imaging speed over those of time-domain OCT systems. Thehigh-speed of Fourier-domain OCT has made it easier to image not onlystructure, but also blood flow. This functional extension was firstdemonstrated by Doppler OCT which images blood flow by evaluating phasedifferences between adjacent A-line scans. Although Doppler OCT is ableto image and measure blood flow in larger blood vessels, it hasdifficulty distinguishing the slow flow in small blood vessels frombiological motion in extravascular tissue. In the imaging of retinalblood vessels, Doppler OCT faces the additional constraint that mostvessels are nearly perpendicular to the OCT beam, and therefore thedetectability of the Doppler shift signal depends critically on the beamincident angle. Thus, other techniques that do not depend on beamincidence angle are particularly attractive for retinal and choroidalangiography.

Several OCT-based techniques have been successfully developed to imagemicrovascular networks in human eyes in vivo. One example is opticalmicroangiography (OMAG), which can resolve the fine vasculature in bothretinal and choroid layers. OMAG works by using a modified Hilberttransform to separate the scattering signals from static and movingscatters. By applying the OMAG algorithm along the slow scanning axis,high sensitivity imaging of capillary flow can be achieved. However, thehigh-sensitivity of OMAG requires precise removal of bulk-motion byresolving the Doppler phase shift. Thus, it is susceptible to artifactsfrom system or biological phase instability. Other related methods suchas phase variance and Doppler variance have been developed to detectsmall phase variations from microvascular flow. These methods do notrequire non-perpendicular beam incidence and can detect both transverseand axial flow. They have also been successful in visualizing retinaland choroidal microvascular networks. However, these phase-based methodsalso require very precise removal of background Doppler phase shifts dueto the axial movement of bulk tissue. Artifacts can also be introducedby phase noise in the OCT system and transverse tissue motion, and thesealso need to be removed.

To date, most of the aforementioned approaches have been based onspectral OCT, which provides high phase stability to evaluate phaseshifts or differentiates the phase contrast resulting from blood flow.Compared with spectral OCT, swept-source OCT introduces another sourceof phase variation from the cycle-to-cycle tuning and timingvariabilities. This makes phase-based angiography noisier. To usephase-based angiography methods on swept-source OCT, more complexapproaches to reduce system phase noise are required. On the other hand,swept-source OCT offers several advantages over spectral OCT, such aslonger imaging range, less depth-dependent signal roll-off, and lessmotion-induced signal loss due to fringe washout. Thus an angiographymethod that does not depend on phase stability may be the best choice tofully exploit the advantages of swept-source OCT. In this context,amplitude-based OCT signal analysis may be advantageous for ophthalmicmicrovascular imaging.

One difficulty associated with OCT's application in microvascularimaging comes from the prevalent existence of speckle in OCT imagesobtained from in vivo or in situ biological samples. Speckle is theresult of the coherent summation of light waves with random path lengthsand it is often considered as a noise source which degrades the qualityof OCT images. Various methods have been developed to reduce speckle inspatial domain, such as angle compounding, spectral compounding, andstrain compounding. Speckle adds to “salt-and-pepper-like” noise to OCTimages and induces random modulation to interferometric spectra whichcan significantly reduce contrast.

In spite of being a noise source, speckle also carries information.Speckle pattern forms due to the coherent superposition of randomphasors. As a result of speckle, the OCT signal becomes random in anarea that is macroscopically uniform. If a sample under imaging isstatic, the speckle pattern is temporally stationary. However, whenphotons are backscattered by moving particles, such as red blood cellsin flowing blood, the formed speckle pattern will change rapidly overtime. Speckle decorrelation has long been used in ultrasound imaging andin laser speckle technique to detect optical scattering from movingparticles such as red blood cells. This phenomenon is also clearlyexhibited by real-time OCT reflectance images. The scattering pattern ofblood flow varies rapidly over time. This is caused by the fact that theflow stream drives randomly distributed blood cells through the imagingvolume (voxel), resulting in decorrelation of the received backscatteredsignals that are a function of scatterer displacement over time. Thecontrast between the decorrelation of blood flow and static tissue maybe used to extract flow signals for angiography.

The speckle phenomenon has been used in speckle variance OCT for thevisualization of microvasculature. Speckle patterns at areas withflowing blood have a large temporal variation, which can be quantifiedby inter-frame speckle variance. This technique termed “specklevariance” has been used with swept-source OCT demonstrating asignificant improvement in capillary detection in tumors by calculationof the variance of the OCT signal intensity. A key advantage of thespeckle variance method is that it does not suffer from phase noiseartifacts and does not require complex phase correction methods.Correlation mapping is another amplitude-based method that has alsorecently demonstrated swept-source OCT mapping of animal cerebral andhuman cutaneous microcirculation in vivo. These amplitude-basedangiography methods are well suited to swept-source OCT and offervaluable alternatives to the phase-based methods. However, such methodsstill suffer from bulk-motion noise in the axial dimension where OCTresolution is very high. Therefore, an amplitude-based swept-sourceangiography method that is able to reduce bulk-motion noise withoutsignificant sacrifice in the flow signal would be optimal. For example,imaging of retinal and choroidal flow could be particularly improvedwith such noise reduction, as in the ocular fundus the flow signal ispredominantly in the transverse rather than axial dimension.

SUMMARY

Disclosed herein are methods, apparatuses, and systems foramplitude-based OCT angiography that utilize the splitting of the OCTspectrum to reduce the predominant bulk-motion noise in the axialdimension where OCT resolution is very high. For example, such methods,apparatuses and systems can be called “split-spectrumamplitude-decorrelation angiography” (SSADA).

A novel OCT angiography technique based on the decorrelation of OCTsignal amplitude due to flow is described herein. By splitting the fullOCT spectral interferograms into several wavenumber bands, the OCTresolution cell in each band is made isotropic and less susceptible toaxial motion noise. Recombining the decorrelation images from thewavenumber bands yields angiograms that use the full information in theentire OCT spectral range. The isotropic resolution cell resulting fromof the SSADA can be used to quantify flow with equal sensitivity toaxial and transverse flow. SSADA can improve signal to noise ratio (SNR)of flow detection and vascular connectivity compared to existingamplitude-based swept-source angiography methods. Utilizing SSADA fornon-invasive angiography of the ocular circulatory beds (e.g., peri- andparafoveal retinal microcirculatory networks) can be useful in thediagnosis and management of important blinding diseases such asglaucoma, diabetic retinopathy and age-related macular degeneration.SSADA can also be useful outside the eye, for example in theinvestigation of cerebral circulation and tumor angiogenesis.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will be readily understood by thefollowing detailed description in conjunction with the accompanyingdrawings.

Embodiments of the invention are illustrated by way of example and notby way of limitation in the figures of the accompanying drawings.

FIG. 1 is a chart comparing prior art techniques and the presentinvention with regard to vascular connectivity and decorrelationsignal/noise (DSNR).

FIG. 2 schematically illustrates modification of an OCT imagingresolution cell to create an isotropic resolution cell utilizing aband-pass filter and the present invention.

FIG. 3 schematically illustrates M-B-scan mode for acquiring OCTspectrum.

FIG. 4 is a flowchart showing an exemplary method for creating adecorrelation (flow) image that uses split-spectrum techniques and thefull information in the entire OCT spectral range.

FIG. 5 is a flowchart showing additional exemplary methods of theexemplary method of FIG. 4.

FIG. 6 schematically illustrates a 2D spectral interferogram split intodifferent frequency bands as described in the present invention.

FIG. 7 schematically illustrates the methods of FIG. 4 and FIG. 5 forcreating a decorrelation (flow) image that uses split-spectrumtechniques and the full information in the entire OCT spectral range.

FIG. 8 is a flowchart showing an exemplary method for eliminatingdecorrelation images with excessive motion noise.

FIG. 9 schematically illustrates an in vivo imaging system forcollecting image information.

FIG. 10 illustrates an embodiment of an in vivo imaging system inaccordance with various embodiments of the present invention.

FIG. 11 illustrates an embodiment of an article of manufacture for invivo imaging in accordance with various embodiments of the presentinvention.

FIG. 12 illustrates in vivo 3-D volumetric structure images of the opticnerve head using imaging methods in accordance with various embodimentsof the present invention.

FIG. 13 illustrates in vivo 3-D volumetric structure images of themacula using methods in accordance with various embodiments of thepresent invention.

FIG. 14 illustrates in vivo images of macular retinal circulation usingmethods in accordance with prior art methods and in accordance withvarious embodiments of the present invention.

FIG. 15 illustrates in vivo images depicting vascular connectivity andsignal to noise ration (SNR) using methods in accordance with prior artmethods and in accordance with various embodiment of the presentinvention.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof and in which is shown byway of illustration embodiments in which the invention may be practiced.It is to be understood that other embodiments may be utilized andstructural or logical changes may be made without departing from thescope of the present invention. Therefore, the following detaileddescription is not to be taken in a limiting sense, and the scope ofembodiments in accordance with the present invention is defined by theappended claims and their equivalents.

Various operations may be described as multiple discrete operations inturn, in a manner that may be helpful in understanding embodiments ofthe present invention; however, the order of description should not beconstrued to imply that these operations are order dependent.

The description may use perspective-based descriptions such as up/down,back/front, and top/bottom. Such descriptions are merely used tofacilitate the discussion and are not intended to restrict theapplication of embodiments of the present invention.

The description may use the phrases “in an embodiment,” or “inembodiments,” which may each refer to one or more of the same ordifferent embodiments. Furthermore, the terms “comprising,” “including,”“having,” and the like, as used with respect to embodiments of thepresent invention, are synonymous.

A phrase in the form of “A/B” means “A or B.” A phrase in the form “Aand/or B” means “(A), (B), or (A and B).” A phrase in the form “at leastone of A, B and C” means “(A), (B), (C), (A and B), (A and C), (B and C)or (A, B and C).” A phrase in the form “(A) B” means “(B) or (A B),”that is, A is optional. In various embodiments of the present invention,methods, apparatuses, and systems for biomedical imaging are provided.In exemplary embodiments of the present invention, a computing systemmay be endowed with one or more components of the disclosed articles ofmanufacture and/or systems and may be employed to perform one or moremethods as disclosed herein.

In various embodiments, structure and/or flow information of a samplemay be obtained using optical coherence tomography (OCT) (structure) andOCT angiography (structure and flow) imaging based on the detection ofspectral interference. Such imaging may be two-dimensional (2-D) orthree-dimensional (3-D), depending on the application. Structuralimaging may be of an extended depth range relative to prior art methods,and flow imaging may be performed in real time. One or both ofstructural imaging and flow imaging as disclosed herein may be enlistedfor producing 2-D or 3-D images.

Unless otherwise noted or explained, all technical and scientific termsused herein are used according to conventional usage and have the samemeaning as commonly understood by one of ordinary skill in the art whichthe disclosure belongs. Although methods, systems, andapparatuses/materials similar or equivalent to those described hereincan be used in the practice or testing of the present disclosure,suitable methods, systems, and apparatuses/materials are describedbelow.

All publications, patent applications, patents, and other referencesmentioned herein are incorporated by reference in their entirety. Incase of conflict, the present specification, including explanation ofterms, will control. In addition, the methods, systems, apparatuses,materials, and examples are illustrative only and not intended to belimiting.

In order to facilitate review of the various embodiments of thedisclosure, the following explanation of specific terms is provided:

A-scan: A reflectivity profile that contains information about spatialdimensions and location of structures with an item of interest (e.g., anaxial depth scan).

Autocorrelation: A cross-correlation of a signal with itself; thesimilarity between observations as a function of the time separationbetween them. For example, autocorrelation can be used to find repeatingpatterns, such as the presence of a periodic signal which has beenburied under noise, or used to identify the missing fundamentalfrequency in a signal implied by its harmonic frequencies.

B-scan: A cross-sectional tomograph that may be achieved by laterallycombining a series of axial depth scans (e.g., A-scans).

Cross-correlation: A measure of similarity of two waveforms as afunction of a time-lag applied to one of the waveforms.

Decorrelation: A process that is used to reduce autocorrelation within asignal, or cross-correlation within a set of signals, while preservingother aspects of the signal. For example, decorrelation can be used toenhance differences found in each pixel of an image. A measure of a lackof correlation or similarity between corresponding pixels in two imagescan also describe decorrelation. The end result of a decorrelationprocess is that faint information within a signal may be enhanced tobring out (e.g., present) subtle differences that may be meaningful. Forexample, one can calculate decorrelation to find a difference betweenimages.

Illustrated in FIG. 1 is a comparison chart 100 of prior artamplitude-based OCT signal analysis methods and the present inventionbased on vascular connectivity and decorrelation signal/noise (DSNR).Full-spectrum decorrelation method 100, for example, can be utilized asthe baseline value for comparison purposes, however, as describedpreviously, it is sensitive to axial bulk motion causing significantnoise in the resulting images produced. In pixel averaging method 112the signal in several adjacent pixels is combined resulting in animprovement of decorrelation signal-to-noise ratio (DSNR). The improvedDSNR of pixel averaging method 112 in turn leads to higher qualityimages of microcirculation (compared to full-spectrum decorrelationmethod 100), which can be assessed by measuring the vascular of themicrovascular network revealed in the OCT angiograms. As describedherein, the present invention of split-spectrum decorrelation 122further improves DSNR (compared to the improvement offered by pixelaveraging method 112) by reducing the noise due to axial bulk motion.This can be accomplished by the methods described herein below (e.g.,reducing the axial dimension of the effective resolution cell). Theimproved DSNR of split-spectrum decorrelation method 122 in turn leadsto even higher quality images of microcirculation (compared tofull-spectrum decorrelation method 100 and pixel averaging method 112),which can be assessed by measuring the vascular of the microvascularnetwork revealed in the OCT angiograms. Such an improvement, can allowfor images and information useful in for diagnostic and management ofdiseases in the eye, as well as investigations and analysis ofcirculation, angiogenesis and the other blood flow imaging analysis.Additionally, the split-spectrum decorrelation 122 could be used toobtain angiography images that could be used to replace fluorescein andindocyanine green angiographies, with the additional advantage of beingintrinsically 3-dimensional rather than 2-dimensional. Additional usescan include, but not be limited to, imaging of blood flow in otherbiological tissue and the imaging of flow in any system, living ornonliving.

In more detail, prior art full-spectrum decorrelation 102 achievesdecorrelation purely through process the amplitude signal and does notrequire phase information. To evaluate the flow signals coming from thescattering tissue, an average decorrelation image

at each position is obtained by averaging N−1 decorrelation image framescomputed from N reflectance amplitude images frames from M-B modescanning. Each decorrelation frame is computed from 2 adjacent amplitudeframes:

and

. Using the full spectrum decorrelation method 102, the decorrelationimage it is given by the following equation

$\begin{matrix}{\mspace{79mu} {{{\overset{\_}{D}\left( {x,z} \right)} = {1 - {\frac{1}{N - 1}{\sum\limits_{n = 1}^{N - 1}{\frac{\text{?}\left( {x,z} \right)\text{?}\left( {x,z} \right)}{\left\lbrack {{\frac{1}{2}\text{?}\left( {x,z} \right)} + {\frac{1}{2}\text{?}\left( {x,z} \right)^{2}}} \right\rbrack}\left( {N = 8} \right)}}}}}{\text{?}\text{indicates text missing or illegible when filed}}}} & (1)\end{matrix}$

where x and z are lateral and depth indices of the B-scan images and ndenotes the B-scan slice index. In this full spectrum equation, thedecorrelation signal-to-noise ratio acquired from full spectrum can onlybe increased by increasing the number N of B-scans taken at the sameposition. However, more scans require more imaging time which may not bepractical.

In more detail, prior art pixel averaging method 112 can producedecorrelation images given by the following equation

$\begin{matrix}{{{\overset{\_}{D}\left( {x,z} \right)} = {1 - {\frac{1}{N - 1}\frac{1}{PQ}\text{?}\frac{\text{?}\left( {x + \text{?} + q} \right)\text{?}\left( {x + \text{?} + q} \right)}{\left\lbrack {{\text{?}\left( {x + \text{?} + q} \right)^{2}} + {\frac{1}{2}\text{?}\left( {x + \text{?} + q} \right)^{2}}} \right\rbrack}}}}\mspace{79mu} \left( {{P = 1},{Q = 4},{N = 8}} \right){\text{?}\text{indicates text missing or illegible when filed}}} & (2)\end{matrix}$

where P and Q are the averaging window widths in the X and Z directions,as described in .J. Enfield, E. Jonathan, and M. Leahy, “In vivo imagingof the microcirculation of the volar forearm using correlation mappingoptical coherence tomography (cmoct),” Biomed. Opt. Express 2(5),1184-1193 (2011). To suppress the spurious noise and discontinuities inthe vasculature, P by Q window moving average can be implemented overthe X-Z 2D map. To fairly compare the prior art pixel averaging method112 with the split-spectrum decorrelation 122 described herein, a 1 by 4window can be created, which means pixel-averaging is only applied alongthe Z direction, the same direction used for splitting the spectrum insplit-spectrum decorrelation 122.

In more detail, split-spectrum decorrelation 122 can producedecorrelation images given by the following equation,

$\begin{matrix}{{{\overset{\_}{D}\left( {x,z} \right)} = {1 - {\frac{1}{N - 1}\frac{1}{M}{\sum\limits_{n = 1}^{N - 1}{\sum\limits_{m = 1}^{M}{\frac{\text{?}\left( {x,z} \right)\text{?}\left( {x,z} \right)}{\left\lbrack {{\frac{1}{2}\text{?}\left( {x,z} \right)^{2}} + {\frac{1}{2}\text{?}\left( {x,z} \right)^{2}}} \right\rbrack}\left( {{M = 4},{N = 8}} \right)}}}}}}{\text{?}\text{indicates text missing or illegible when filed}}} & (3)\end{matrix}$

After splitting the spectrum by applying M (for example, M can=4 asdescribed in an exemplary example below) equally spaced bandpassfilters, M individual decorrelation images can be obtained between eachpair of B-scans, which can then be averaged along both the lateral (X)and axial (Z) directions to increase DSNR. In split-spectrumdecorrelation 122, the average decorrelation image D(x,z) can bedescribed as the average of decorrelation images from M spectral bands.By increasing the number M (up to a point), the decorrelationsignal-to-noise ratio can be improved without increasing the scanacquisition time.

Whichever decorrelation method is used (full-spectrum 102,pixel-averaging 112, and split-spectrum 122) the resulting averagedecorrelation image frame D(x,z) should be a value between zero and one,indicating weak and strong decorrelation, respectively. By describingthe decorrelation methods in such detail above, it is possible tocompare the methods to one another based on the resulting decorrelationimages obtained as illustrated in chart 100 of FIG. 1. Thesplit-spectrum method 122 suppresses noise from axial bulk motion and,in addition, makes use of information in the full k spectrum resultingin superior decorrelation signal-to-noise ratio for flow detection.Utilizing the split-spectrum method 122, axial bulk motion can besuppressed by the use of spectral (k) bandpass filters that increase theaxial dimension of the resolution cell so that it can be equal (orsubstantially equal) to the transverse dimension of the resolution cell.

Illustrated in FIG. 2 is diagram 200 visually depicting modification ofan OCT imaging resolution cell 202 via two distinct and separatetechniques (band-pass filtering 204 and split-spectrum 206) to create anisotropic resolution cell 208. Each pixel in a B-scan OCT image isformed from backscattered signals of a 3D volume in space, referred toas a resolution cell (e.g., imaging resolution cell 202 in FIG. 2). Thestatistical changes in the envelope intensity are related to the motionof scatterers through the OCT resolution cell. For a typicalswept-source OCT setup, the axial (Z direction) resolution, determinedby the source central wavelength and its spectral bandwidth, is muchhigher than the lateral resolution determined by the laser beam profilein both X and Y directions. For example, in common swept source OCTsystems, using the full-width-half-maximum (FWHM) amplitude profiledefinition, the axial resolution (˜5 μm) is four times higher than thelateral resolution (˜18 μm) if both are defined asfull-width-half-maximum amplitude profiles (e.g., imaging resolutioncell 202 depicts x=y=4z). This anisotropic resolution cell, with higheraxial than transverse resolution, will result in higher decorrelationsensitivity for axial motion. In the fundus, ocular pulsation related toheart beat, driven by the retrobulbar orbital tissue, mainly occursalong the axial direction. The anisotropic resolution cell of retinalOCT imaging is very sensitive to this axial motion noise. On the otherhand, retinal and choroidal blood flow vectors are primarily transverseto the OCT beam, along the wider (less sensitive) dimensions of the OCTresolution cell. Therefore, to improve the signal-to-noise ratio (SNR)of flow detection, it is desirable to lower the axial resolution anddampen the axial decorrelation sensitivity. This reduces the axialmotion noise without sacrificing the transverse flow signal.

One straightforward way to achieve this resolution modification isband-pass filtering of the spectral interferogram (e.g., band-passfiltering 204). Unfortunately, this also sacrifices most of the speckleinformation in the spectral interferogram and decreases the flow signal.Thus, this is not an effective way to increase the SNR of flow(decorrelation) detection. A better way to decrease axial resolutionwithout losing any speckle information is to split the spectrum intodifferent frequency bands (e.g., split-spectrum 206) and calculatedecorrelation in each band separately. The decorrelation (flow) imagesfrom the multiple spectral bands can then be averaged together to makefull use of the speckle information in the entire OCT spectrum. Thedetails of the split-spectrum procedure are explained herein and below(e.g., split-spectrum decorrelation 122 of FIG. 1 can be utilized).

Illustrated in FIG. 3 is a visual 300 of one 3D volumetric data 302comprising data obtained via an exemplary embodiment M-B-scan mode froman OCT system. N consecutive B-scans at a single Y position compriseM-B-scan 306 (e.g., in some exemplary embodiments described herein,N=eight (8), but is not limited to any specific N). Therefore, for each3D volumetric data 302, in the fast scan (x) axis, a single B-scancomprises a plurality of A-scans 304, and in the slow scan (y) axis,there are a number of M-B-scans 306 comprising N consecutive B-scans.

FIG. 4 shows an exemplary method 400 for creating a decorrelation (flow)image that uses split-spectrum techniques and the full information inthe entire OCT spectral range. The method 400 can be performed, forexample, by in vivo imaging systems described herein below. Portions ofmethod 400 and any of the other methods (or portion of methods)described herein can be performed by computer-executable instructionsstored on computer-readable media and articles of manufacture for invivo imaging.

At 402, M-B scans of OCT spectrum are received. For example, M-B scansas depicted in visual 300 of FIG. 3 can be received from an OCT in vivoimaging system.

At 404, M spectral bands can be created from the M-B scans of OCTspectrum 402. For example, split spectrum 206 of FIG. 2 can be utilizedto create the M spectral bands.

At 406, averaged decorrelation images for each spectral band of the Mspectral bands can be created. For example, split spectrum decorrelation122 described in FIG. 1 can be utilized to create decorrelation imagesfor the M spectral bands and then for each spectral band thosedecorrelation images can be averaged.

At 408, the averaged decorrelation images for each spectral band createdat 406 can be averaged to create a single final image (e.g., finaldecorrelation image) 410.

FIG. 5 shows additional exemplary methods 500, including reference tosimilar methods within method 400 of FIG. 4, for creating adecorrelation (flow) image that uses split-spectrum techniques and thefull information in the entire OCT spectral range. The method 500 can beperformed, for example, by in vivo imaging systems described hereinbelow. Portions of method 500 and any of the other methods (or portionof methods) described herein can be performed by computer-executableinstructions stored on computer-readable media and articles ofmanufacture for in vivo imaging.

FIG. 6 schematically illustrates via visual 600 a 2D spectralinterferogram split into different frequency bands as described inmethods 400 of FIGS. 4 and 500 of FIG. 5.

FIG. 7 schematically illustrates via visual 700 the methods 400 of FIGS.4 and 500 of FIG. 5 for creating a decorrelatin (flow) image that usessplit-spectrum techniques and the full information in the entire OCTspectral range.

FIG. 8 is a flowchart 800 showing an exemplary method for eliminatingdecorrelation images with excessive motion noise (e.g., as described inmethod 500 of FIG. 5).

Continuing with the method 500 of FIG. 5, at 502, M-B scans of OCTspectrum are received. For example, M-B scans of OCT spectrum 402 can bereceived from an OCT in vivo imaging system, as depicted in FIG. 7. Inmore detail, for example, spectral interference signal recorded by ahigh speed digitizer in swept-source OCT, after subtracting backgroundand autocorrelation terms, can be received and simply given by thefollowing equation

$\begin{matrix}{{I\left( {x,k} \right)} = {\int_{- \infty}^{\infty}{{R(k)}{A\left( {x,k,z} \right)}{\cos \left( {2\; k\; z} \right)}\ {z}}}} & (4)\end{matrix}$

where x is the transverse position of focus beam spot on the samplealong the fast scan axis, k is the wavenumber,

is the light intensity,

is the amplitude of light reflected from the reference arm,

is the amplitude of the light backscattered from the sample, and z isthe optical delay mismatch between the sample reflections and thereference reflection in the free space equivalent.

At 504, overlapping filters (M) covering the entire spectrum can becreated. Additionally, at 506, band pass filtering along k can beconducted. Collectively, creating overlapping filters 504 and band pastfiltering 506 can result in creating M spectral bands 507 as depicted inFIG. 7 (e.g., as described in creating M spectral bands 404 in method400 of FIG. 4). Following along with the example provided above of thespectral interference signal represented by equation (4), the Gaussianshape above the 2D interferogram

(e.g., 2 D interferogram 605 of FIG. 6) can be used to express thereceived interferometric fringe at one position. The bandwidth of thisfull-spectrum fringe can first be defined, and then a filter bankcreated to divide this full-spectrum fringe into different bands (e.g.,creating overlapping filters (M) 504 of method 500). The specificationsof this filter bank can depend on several factors, including, but notlimited to, 1) filter type, 2) bandwidth of each filter, 3) overlapbetween different bands, and 4) number of bands. In one exemplaryembodiment, a Gaussian filter can be introduced whose function wasdefined by the following equation

$\begin{matrix}{\mspace{79mu} {{{G(n)} = {\exp \left\lbrack {- \frac{\text{?}}{\text{?}}} \right\rbrack}}{\text{?}\text{indicates text missing or illegible when filed}}}} & (5)\end{matrix}$

where n is the spectral element number that varies from 1 to 1400 and islinearly mapped to wavenumber k. The range of sampled k can be 10000 to9091 cm⁻¹, corresponding to a wavelength range of 1000 to 1100 nm. Thebandwidth, referred to as “BW,” (e.g., as depicted in 604 of FIG. 600)of the full spectrum can be 69 nm, which can provide a FWHM axialspatial resolution of 5.3 μm. m is the position of the spectral peak. Inan exemplary embodiment, the peaks of the spectral Gaussian filters canbe placed at 9784, 9625, 9466, and 9307 cm⁻¹. σ² is the variance of theGaussian filter in terms of the number of spectral elements. In anexemplary embodiment, the FWHM amplitude bandwidth, referred to as “bw,”of the bandpass filters can equal to 2√{square root over (2/n2)}σ,covering 378 spectral elements, corresponding to a wavelength range of27 nm or a wavenumber range of 245 cm⁻¹. The four (4) bandpass filters(e.g., as depicted in 608, 610, 612, and 614 of FIG. 6), described insuch an exemplary embodiment, can overlap so that none of the frequencycomponents of the original signal are lost in the processing. FIG. 6visually displays a 2D spectral interferogram 605 split at 606 (e.g.,via 404 of method 400 of FIG. 4) into four new spectra with smaller kbandwidth, with “BW” 604 indicating the bandwidth of a full-spectrumfilter and multiple “bw”s 608, 610, 612, and 614 being the bandwidth ofmultiple Guassian filters, respectively, and regions of non-zero valuesin the data block are indicated by the dark shaded patterns 616, 618,620, and 622 (similarly visually depicted, for example in FIG. 7).

At 508, the M spectral bands 507 from each individual frequency band canbe passed into conventional Fourier-domain OCT algorithms to Fouriertransform along k. Additionally, phase information can be dropped toresult in amplitude information for each spectral band 509 (e.g., asdepicted in FIG. 7). For example, the OCT signals therefore can bedirectly calculated from the decomposed interferograms I′(x,k) byapplying Fourier transform upon wavenumber k. The computed OCT signalcan be a complex function, I(x,z) which can be written as the followingequation

$\begin{matrix}{{I\left( {x,z} \right)} = {{{FFT}\left( {I^{\prime}\left( {x,k} \right)} \right)} = {{A\left( {x,z} \right)}{\exp \left\lbrack {{\phi}\left( {x,z} \right)} \right\rbrack}}}} & (6)\end{matrix}$

where

is the phase of the analytic signal

. The amplitudes of the OCT signals,

, can be used while the phase information can be selectivelydisregarded.

At 510, a fixed value can be set for removal of high decorrelationgenerated by background noise. Decorrelation of OCT signal amplitudebetween B-scans taken at the same nominal position can be caused byseveral sources: (1) flow, (2) bulk tissue motion or scanner positionerror, and (3) background noise. To help accentuate true flowmeasurement in the images created and improve the signal-to-noise ratiofor flow detection, removal of high decorrelation generated bybackground noise is desirable. Background noise is random and thereforehas high decorrelation between B-scan frames. Noise predominates inpixels with low OCT signal amplitude and therefore flow cannot beassessed in these pixels with any accuracy. A fixed decorrelation valueof zero (0) can be assigned to these pixels with low OCT signalamplitude. For example, this can be achieved by setting the low signalpixels a constant amplitude value. The threshold value, for example, canthen be chosen to be two standard deviations above the mean backgroundvalue measured when the sample beam was blocked.

At 512, decorrelation calculation can be obtained between adjacentamplitude frames. For example, split-spectrum decorrelation 122 asdescribed in FIG. 1 can be utilized to produce decorrelation images foreach spectral band 513 (e.g., as depicted in FIG. 7 visually).

At 514, decorrelation images for each spectral band 513 having excessivemotion noise can be eliminated. To help accentuate true flow measurementin the images created and improve the signal-to-noise ratio for flowdetection, removal of decorrelation generated by bulk tissue motion orscanner position is desirable. Saccadic and micro-saccadic eye movementsare rapid and cause a high degree of decorrelation between B-scans, asdepicted, for example, in flowchart 800 of FIG. 8. Such movements can beseen in visual 802 which displays three frames of a set of seven (7)decorrelation images 804 (Dn) of the region around the optic nerve head(ONH), computed from eight (8) OCT B-scans at the same Y location. Eachdecorrelation image frame depicted can be calculated from a pair ofadjacent B-scan amplitude frames, for example as described using themethods described above. In six (6) of the seven (7) decorrelationframes, flow pixels could be distinguished from non-flow pixels by theirhigher decorrelation values. However, in frame D4 806, both flow(vessel) and non-flow (bulk tissue) pixels had high decorrelation valuespossibly due to rapid eye movement (e.g., saccadic). To detect bulkmotion, the median decorrelation value in the highly reflective portionof the imaged tissue structures (between the region noted as 808) can bedetermined. High bulk motion in frame D4 806 can be detected by highmedian decorrelation value in pixel histogram analysis 810. Histogramanalysis can be performed within a high reflectivity band starting atthe retinal inner limiting membrane and spanning 30 pixels below (withinregion 808 of 802. By comparing the median decorrelation value 814 to apreset threshold 812 (e.g., in one exemplary embodiment the thresholdwas set at 0.15, two standard deviations above the mean mediandecorrelation value), it can be determined that a frame (e.g. frame D4)is a statistical outlier and should be eliminated. Visual 816 depictsthe result after the removal of the outlier frame D4.

At 516, the decorrelation images at each spectral band that remain afterimages with excessive motion noise have been removed can be averaged tocreate an average decorrelation image for each spectral band, thereforeresulting in multiple averaged decorrelation images (i.e., one averagedecorrelation for each spectral band as visualized in FIG. 7).

At 518, the averaged decorrelation images from M spectral bands areaveraged to create one final decorrelation image 410 (e.g., asvisualized in FIG. 7 and also described in method 400, step 408 of FIG.4).

Returning back to flowchart 800 of FIG. 8, after removing frame D4 806as an outlier, the remaining six (6) decorrelation images can beaveraged to produce a cleaned decorrelation image 818 which displayshigh contrast between flow pixels (e.g., bright area in retinal vesselsand choroid) and non-flow dark regions. An uncleaned decorrelation image820 depicts a final decorrelation image had outlier frame D4 806remained showing less contrast between flow (vessels) and non-flow(static tissue) pixels compared to the cleaned decorrelation image 818,as evident by the lack of completely dark space between retinal vesselsin the peripapillary areas circled at 822 and 824.

Utilizing method 500, a 3D dataset comprising a stack of two hundred(200) corrected average decorrelation cross-sectional images, along withthe associated average reflectance images, that together spans 3 mm inthe slow transverse scan (Y) direction can be obtained. In someembodiments it may be desirable to separate the 3D data into retinal andchoroidal regions with the dividing boundary set at the retina pigmentepithelium (RPE). The depth (Z position) of the highly reflective RPEcan be identified through the analysis of the reflectance andreflectance gradient profiles in depth. The region above the RPE is theretinal layer and the region below is the choroidal layer. The en faceX-Y projection angiograms can then be produced by selecting the maximumdecorrelation value along the axial (Z) direction in each layer. In ONHscans, the RPE depth just outside the disc boundary can be used to setan interpolated RPE plane inside the disc.

FIG. 9 schematically illustrates an in vivo imaging system 900 forcollecting OCT image information. For example, a high-speed swept-sourceOCT system 900 (e.g., as described in B. Potsaid, B. Baumann, D. Huang,S. Barry, A. E. Cable, J. S. Schuman, J. S. Duker, and J. G. Fujimoto,“Ultrahigh speed 1050 nm swept source/fourier domain oct retinal andanterior segment imaging at 100,000 to 400,000 axial scans per second,”Opt. Express 18(19), 20029-20048 (2010)) can used to demonstrate themethods described above for imaging of microcirculation in the humanocular fundus. High speed swept-source OCT system 900 comprises atunable laser 901. For example, tunable laser 901 (e.g., a tunable laserfrom Axsun Technologies, Inc, Billerica, Mass., USA) may have awavelength of 1050 nm with 100 nm tuning range, a tuning cycle with arepetition rate of 100 kHz and a duty cycle of 50%. Such OCT system 900can produce a measured axial resolution of 5.3 μm(full-width-half-maximum amplitude profile) and an imaging range of 2.9mm in tissue. Light from swept source 901 can be coupled into a two bytwo fiber coupler 902 through single mode optical fiber. One portion ofthe light (e.g., 70%) can proceed to the sample arm (i.e., the patientinterface), and the other portion of the light (e.g., 30%) can proceedto the reference arm.

In the sample arm, a sample arm polarization control unit 903 can beused to adjust light polarization state. The exit light from the fibercoupler 902 can then be couple with a retinal scanner whereby the lightis collimated by sample arm collimating lens 904 and reflected by mirror905 and two dimensional galvo scanner 909 (e.g., an XY galvonanometerscanner). Two lenses, first lense 906 (e.g., an objective lense) andsecond lense 907 (e.g., an ocular lense) can relay probe beam reflectedby galvo scanner 909 into a human eye 908. For example, a focused spotdiameter of 18 μm (full-width-half-maximum amplitude profile) can becalculated on the retinal plane based on an eye model. The average lightpower (i.e., output power of the laser) onto human eye can be 1.9 mW,which is consistent with safe ocular exposure limit set by the AmericanNational Standard Institute (ANSI).

The reference arm can comprise a first reference arm collimating lens913, a water cell 912, a retro-reflector 911, a glass plate 914 and asecond reference arm collimating lens 915. Glass plate 914 can be usedto balance the dispersion between the OCT sample arm and reference arm.Water cell 912 can be used to compensate the influence of dispersion inthe human eye 908. Retro-reflector 911 can be mounted on a translationstage 910 which can be moved to adjust the path length in the referencearm.

Light from sample and reference arm can interfere at beam splitter 917.A reference arm polarization control unit 916 can be used to adjust thebeam polarization state in the reference arm to maximum interferencesignal. The optical interference signal from beam splitter 917 (e.g., a50/50 coupler) can be detected by a balanced detector 918 (e.g., abalanced receiver manufactured by Thorlabs, Inc, Newton, N.J., USA),sampled by an analog digital conversion unit 919 (e.g., a high speeddigitizer manufactured by Innovative Integration, Inc.) and transferredinto computer 920 for processing. For example, computer 920 can be usedfor storing instruction for and implementing the methods describedherein. Interference fringes, for example, can be recorded by analogdigital conversion unit 919 at 400 MHz with 14-bit resolution, with theacquisition driven by the optical clock output of tunable laser 901. Insuch an exemplary setup, imaging system 900, sensitivity can be measuredwith a mirror and neutral density filter at 95 dB, with a sensitivityroll-off of 4.2 dB/mm.

While a swept-source OCT system has been described above, the technologydisclosed herein can be applied to any Fourier-domain OCT system. InFourier-domain OCT systems the reference mirror is kept stationary andthe interference between the sample and reference reflections arecaptured as spectral interferograms, which are processed byFourier-transform to obtain cross-sectional images. In the spectral OCTimplementation of Fourier-domain OCT, a broad band light source is usedand the spectral interferogram is captured by a grating or prism-basedspectrometer. The spectrometer uses a line camera to detect the spectralinterferogram in a simultaneous manner. In the swept-source OCTimplementation of Fourier-domain OCT, the light source is a laser thatis very rapidly and repetitively tuned across a wide spectrum and thespectral interferogram is captured sequentially. Swept-source OCT canachieve higher speed and the beam can be scanned transversely morerapidly (with less spot overlap between axial scans) without sufferingas much signal loss due to fringe washout compared to otherFourier-domain OCT systems. However, a very high speed spectral OCTsystem could also be utilized with the technology disclosed herein.

Any one or more of various embodiments as previously discussed may beincorporated, in part or in whole, into a system. FIG. 10 illustrates anexemplary embodiment of an in vivo imaging system (e.g. an OCT system)1000 in accordance with various embodiments of the present invention. Inthe embodiments, OCT system 1000 may comprise an OCT apparatus 1002 andone or more processors 1012 coupled thereto. One or more of theprocessors 1012 may be adapted to perform methods in accordance withvarious methods as disclosed herein. In various embodiments, OCT system1000 may comprise a computing apparatus including, for example, apersonal computer in any form, and in various ones of these embodiments,one or more of the processors may be disposed in the computingapparatus. OCT systems in accordance with various embodiments may beadapted to store various information. For instance, an OCT system may beadapted to store parameters and/or instructions for performing one ormore methods as disclosed herein.

In various embodiments, an OCT system may be adapted to allow anoperator to perform various tasks. For example, an OCT system may beadapted to allow an operator to configure and/or launch various ones ofthe above-described methods. In some embodiments, an OCT system may beadapted to generate, or cause to be generated, reports of variousinformation including, for example, reports of the results of scans runon a sample.

In embodiments of OCT systems comprising a display device, data and/orother information may be displayed for an operator. In embodiments, adisplay device may be adapted to receive an input (e.g., by a touchscreen, actuation of an icon, manipulation of an input device such as ajoystick or knob, etc.) and the input may, in some cases, becommunicated (actively and/or passively) to one or more processors. Invarious embodiments, data and/or information may be displayed, and anoperator may input information in response thereto.

Any one or more of various embodiments as previously discussed may beincorporated, in part or in whole, into an article of manufacture. Invarious embodiments and as shown in FIG. 11, an article of manufacture1100 in accordance with various embodiments of the present invention maycomprise a storage medium 1112 and a plurality of programminginstructions 1102 stored in storage medium 1112. In various ones ofthese embodiments, programming instructions 1102 may be adapted toprogram an apparatus to enable the apparatus to perform one or more ofthe previously-discussed methods.

In various embodiments, an OCT image may provide data from which adiagnosis and/or evaluation may be made. In embodiments, suchdeterminations may relate to biologic tissue structure, vasculature,and/or microcirculation. For example, in some embodiments, 3-D in vivoimaging of a biologic tissue and quantifying flow of blood throughindividual vessels therein may be useful in understanding mechanismsbehind a number of disease developments and treatments including, forexample, ischemia, degeneration, trauma, seizures, and various otherneurological diseases. In still other embodiments, an OCT image andtechniques herein disclosed may be used to identify cancer, tumors,dementia, and ophthalmologic diseases/conditions (including, e.g.,glaucoma, diabetic retinopathy, age-related macular degeneration). Stillfurther, in various embodiments, OCT techniques as herein disclosed maybe used for endoscopic imaging or other internal medicine applications.The foregoing illustrative embodiments of diagnosis and/or evaluationare exemplary and thus embodiments of the present invention are notlimited to the embodiments discussed.

Although certain embodiments have been illustrated and described hereinfor purposes of description of the preferred embodiment, it will beappreciated by those of ordinary skill in the art that a wide variety ofalternate and/or equivalent embodiments or implementations calculated toachieve the same purposes may be substituted for the embodiments shownand described without departing from the scope of the present invention.Those with skill in the art will readily appreciate that embodiments inaccordance with the present invention may be implemented in a very widevariety of ways. This application is intended to cover any adaptationsor variations of the embodiments discussed herein. Therefore, it ismanifestly intended that embodiments in accordance with the presentinvention be limited only by the claims and the equivalents thereof.

EXEMPLARY EMBODIMENTS

Macular and ONH imaging were performed on three normal volunteers usinga swept-source OCT system 900 described herein, as approved by anInstitutional Review Board (IRB). For each imaging, the subject's headwas stabilized by chin and forehead rests. A flashing internal fixationtarget was projected by an attenuated pico projector using digital lightprocessing (DLP) technology (Texas Instruments, Dallas, Tex., USA). Theimaging area on the fundus was visualized by the operator usingreal-time en face view of a 3 mm×3 mm OCT preview scan

The swept-source OCT system was operated at 100-kHz axial scanrepetition rate. In the fast transverse scan (X) direction, the B-scanconsisted of 200 A-scans over 3 mm. In the slow transverse scan (Y)direction, there were 200 discrete sampling planes over 3 mm. Eightconsecutive B-scans were acquired at each Y position. This is referredto as the “M-B-scan mode” (e.g., as illustrated in FIG. 3) because itenables detection of motion between consecutive B-scans at the sameposition. Thus, it took 3.2 sec to obtain a 3D volumetric data cubecomprised of 1600 B-scans and 32,0000 A-scans. Under this scanningprotocol, methods described herein were applied to the repeated framesequences at each step. Finally, the 200 calculated B-scan frames werecombined to form 3D blood perfusion images of posterior part of thehuman eye.

FIG. 12 illustrates in vivo 3-D volumetric structure images (3.0 (x)×3.0(y)×2.9 (z) mm) of the optic nerve head (ONH) in the right eye of amyopic individual using imaging methods in accordance with variousembodiments of the present invention. From one 3D volumetric dataset,both reflectance intensity images and decorrelation (angiography) imageswere obtained. For the optical nerve head (ONH) scan, the en facemaximum projection of reflectance intensity 1202 showed the majorretinal blood vessels and the second order branches 1204, but finerbranches and the microcirculation of the retina, choroid, and optic discwere not visible. In the vertical cross-sectional intensity image 1208taken from plane 1206 of projection 1202, the connective tissue struts(bright) and pores (dark) of the lamina cribosa could be visualized deepwithin the optic disc. Around the disc, the retina, choroid, and scleracan be delineated. The ONH angiogram obtained by the methods describedherein showed both many orders of vascular branching as well as themicrocirculatory network. The en face maximum decorrelation projectionangiogram 1210 showed many orders of branching from the central retinalartery and vein, a dense capillary network in the disc, a cilioretinalartery (reference by an arrow in angiogram 1210 at the nasal discmargin), and a near continuous sheet of choroidal vessels around thedisc, much of which could not be visualized well on the en faceintensity image 1202. The vertical SSADA cross-section decorrelationimage 1212 (in the same plane 1206 as 1208 displayed) created showedblood flow in blood vessels in the disc (represented by arrows), retinalvessels, and choroid that form columns from the surface to a depth of˜1.0 mm. It may be unclear if this represents deep penetrating vesselsor if this is represents decorrelation projection artifact. Projectionartifact refers to the fact that light reflected from deeper staticstructures may show decorrelation due to passing through a moresuperficial blood vessel. This type of artifact is evident where theperipapillary retinal vessels seem thicker than they should be, forexample in fly-through movie still frame image 1216 and in decorrelationimage 1212. Due to this artifact, these vessels extended down the fulldepth of the nerve fiber layer (NFL), and the decorrelation signalappeared in the subjacent pigment epithelium (RPE), which should beavascular.

To separately view the retinal vessels and superficial disc vessels,pixels were removed below the level of the peripapillary RPE to removethe choroid. The resulting en face angiogram 1214 showed that thesuperficial vascular network nourishes the disc ends at the discboundary. By comparison, the choroidal circulation formed an almostcontinuous sheet of blood flow under the retina as shown in 1210. The enface images 1202, 1210, and 1214 show RPE atrophy in a temporal crescentjust outside the disc margin. Inside the crescent there was also a smallregion of choriocapillaris atrophy (see the arrow region within 1210).Overlaying the cross-sectional gray scale reflectance intensity imagewith the color scale flow (decorrelation) image showed that the majorretinal branches vessels were at the level of the peripapillary NFL, asshown in fly-through movie still frame image 1216 (i.e., how the disc,retina, and choroid are perfused in a 3D volumetric fashion). It alsoshowed the blood flow within the full thickness of the choroid. Thecombined image 1216 also showed that the deeper disc circulation residesprimarily in the pores of the lamina cribosa and not in the connectivetissue struts. This may be the first time that the disc microcirculationhas been visualized noninvasively in such a comprehensive manner. Thehorizontal line across the image was a result of a fixed patternartifact that originated from the swept laser source.

Another exemplary example utilizing the invention disclosed herein wasdemonstrated in macular angiography. The macular region of the fundus isresponsible for central vision. Capillary dropout in the macular regiondue to diabetic retinopathy is a major cause of vision loss. Focal lossof the choriocapillaris is a possible causative factor in thepathogenesis of both dry and wet age-related macular degeneration, theleading cause of blindness in industrialized nations. Thus macularangiography is important. The technology described herein was used todemonstrate macular angiography of both the retinal and choroidalcirculations in a normal eye as shown in the in vivo 3-D volumetricstructure images (3.0 (x)×3.0 (y)×2.9 (z) mm) of the macula in FIG. 13.

The vascular pattern and capillary networks visualized using thetechnology disclosed herein were similar to those previously reportedusing phase-based OCT angiography techniques. The flow pixels formed acontinuous microcirculatory network in the retina. There was an absenceof vascular network in the foveal avascular zone (as shown in en facemaximum decorrelation projection angiogram 1302) of approximately 600 μmdiameter, in agreement with known anatomy. There were some disconnectedapparent flow pixels within the foveal avascular zone due to noise.Horizontal OCT cross section through the foveal center (upper dashedline in 1302) with merged flow information (decorrelation represented inbright/color scale) and structure information (reflectance intensityrepresented in gray/darker scale) is represented with foveal centerimage 1304. Inspection of foveal center image 1304 shows these falseflow pixels to be decorrelation noise in the high reflectance layers ofthe RPE and photoreceptors. The choriocapillaris layer forms a confluentoverlapping plexus, so it is to be expected that the projection image ofthe choroid circulation (see en face maximum decorrelation projectionangiogram of the choroidal circulation 1306) shows confluent flow.Similar to 1304, a merged horizontal OCT cross section of the inferiormacula (lower dashed line in 1302) is represented with inferior maculaimage 1308. The cross section images 1304 and 1308 showed retinalvessels from the NFL to the outer plexiform layer, in agreement withknown anatomy. The flow in the inner choroid had higher velocity asbased on decorrelation seen in the bright/color scale. The volume wasalso greater than the retinal circulation (as shown in the cross sectionimages 1304 and 1308), again consistent with known physiology that thechoroidal circulation has much higher flow than the retinal circulation.There were signal voids in the outer choroid which may be due to fringewashout from high flow velocity and the shadowing effect of overlyingtissue. The cross section images 1304 and 1308 also showed a few spotsof decorrelation in the RPE layer. These are likely artifacts becausethe RPE is known to be avascular. As mentioned previously, this islikely due to the projection of decorrelation of flow in a proximallayer (i.e., inner retinal layers) onto distal layers with a strongreflected signal (i.e., RPE). There was also a tendency for vessels toform vertical arrays in the inner retina, which may in some instances bedue to the projection artifact as well.

Another exemplary example utilizing the invention disclosed herein wasdemonstrated to appreciate the differences between full-spectrum,pixel-averaging, and split-spectrum techniques (as described in FIG. 1)for decorrelation-based angiography. To obtain angiograms, the methodsdescribed above, in particular with description to FIG. 1 and asdescribed by equations (1)-(3), respectively. For fair comparison,identical motion error reduction, noise threshold, and en faceprojection methods were used.

FIG. 14 illustrates en face angiograms of macular retinal circulationusing methods in accordance with prior art methods full-spectrum (1402)and pixel-averaging (1404) and in accordance with various embodiments ofthe present invention (1408). While the prior art methods and presentinvention provided good visualization of major macular vessels, thecapillary network looked the cleanest and most continuous insplit-spectrum angiogram 108 generated with the split-spectrum presentinvention. The pixel-averaging method producing pixel-averagingangiogram 1404 displays the second cleanest and continuous capillarynetwork. The full-spectrum method producing full-spectrum angiogram 1402showed significantly more disconnected flow pixels that were likely tobe noise. The noise can be most easily appreciated in the fovealavascular zone (inside the yellow circles of 1402A, 1402B, and 1408Cimages of 600-um diameter), which should not have any retinal vessels,including capillaries. In the split-spectrum angiogram 1408, there was anear continuous visualization of the capillary network just outside theavascular zone, while this loop appeared broken up using the other twoprior art techniques. The cross-sectional angiograms for each method asdisplayed in 1402D, 1404E, and 1408F (all scanned across a horizontaldashed line as shown in 1402A showed that the split-spectrum methodprovided the cleanest contrast between distinct retinal vessels and darkbackground. Again, the pixel-averaging method was second best, and thefull-spectrum method showed visible snow-like background noise.

To obtain quantitative figures of merit to compare the threedecorrelation-based angiography techniques, we made use of two pieces ofanatomic knowledge. One is that the retinal vessels form a continuousnetwork, and the other is that there are no retinal vessels within thefoveal avascular zone. FIG. 15 illustrates in vivo images depictingvascular connectivity and signal to noise ratio (SNR) using methods inaccordance with prior art methods and in accordance with variousembodiment of the present invention. In FIG. 15, images 1502A1-1502A4were obtained using the full-spectrum method (all in row 1502), images1504B1-1504B4 were obtained using the pixel-averaging method (all in row1504), and images 1506C1-C4 were obtained using the split-spectrumtechnology described herein. To assess vessel connectivity, theprojection images (1402A, 1404B, and 1408C of FIG. 14) obtained by thethree different methods were converted to binary images (e.g.,binerized) (as shown in the images in first column 1508 of FIG. 15,images 1502A1, 1504B1, and 1506C1) based on a fixed threshold. Then askeletonizing morphological operation (e.g., skeletonized) was appliedto obtain a vascular network made of 1-pixel wide lines and dots (asshown in the images in second column 1510 of FIG. 15, images 1502A2,1504B2, and 1506C2). Next the unconnected flow pixels were separatedfrom the connected flow skeleton (e.g., filtered to remove unconnectedflow pixels) (as shown in the images in third column 1512 of FIG. 15,images 1502A3, 1504B3, and 1506C3). The vascular connectivity wasdefined as the ratio of the number of connected flow pixels to the totalnumber of flow pixels on the skeleton map. Connectivity was analyzed onthe OCT macular angiograms of six eyes of the three participants (seeTable 1 below). A comparison of the three techniques based on pairedt-tests showed that the split-spectrum technology had significantlybetter connectivity relative to the pixel-averaging (p=0.037) andfull-spectrum (p=0.014) techniques. The split-spectrum technologydisclosed herein reduced the number of unconnected flow pixels (18%) bymore than a factor of 2 when compared with the full-spectrum prior arttechnique (39%).

To compute a signal to noise (SNR) for the decorrelation signal, it wasnecessary to define relevant signal and noise regions. For the macula,fortuitously, the central foveal avascular zone (FAZ) is devoid of bloodvessels, including capillaries. The parafoveal capillary networknourishes the fovea and the loss of these capillaries in diabeticretinopathy is an important mechanism in the loss of vision. Thus theratio of decorrelation value in the parafoveal region relative to theFAZ can be a clean and clinically relevant way to compute SNR. In thefourth column 1512 of FIG. 15, images 1502A4, 1504B4, and 1506C4, showdecorrelation SNR, where the noise region was inside the fovealavascular zone (displayed as inner dotted circles with radius R1) andthe signal region was the parafoveal annulus (as displayed the grayedregion between radius R2 and radius R3). The radius of the FAZ (R1) isapproximately 0.3 mm. Therefore, it was chosen that the central FAZ witha radius of 0.3 mm was the noise region and the annular parafovealregion between 0.65 (R2) and 1.00 mm (R3) radii was the signal region.Therefore, the decorrelation signal-to-noise ratio DSNR can be representusing the following formula,

$\begin{matrix}{\mspace{79mu} {{{DSNR} = \frac{\text{?} - \text{?}}{\sqrt{\text{?}}}}{\text{?}\text{indicates text missing or illegible when filed}}}} & (7)\end{matrix}$

where

and

are the average decorrelation values within the parafoveal annulus andFAZ, respectively; and

is the variance of decorrelation values within the FAZ. Thesecomputations were performed over the en face maximum projection images.

The DSNR was analyzed on the OCT macular angiograms performed on sixeyes of the three participants (see Table 1 below). The paired t-testshowed that the DSNR of the split-spectrum technology was significantlyhigher than the pixel-averaging technique (p=0.034) and thefull-spectrum technique (p=0.012). The split-spectrum technologyimproved the DSNR by more than a factor of 2 compared to thefull-spectrum technique.

TABLE 1 Vascular Connectivity and Signal-to-Noise Ratio of ThreeAngiography Algorithms Improvement Amplitude Connectivity of DSNRImprovement decorrelation (mean ± sd) connectivity (mean ± sd) of DSNRfull-spectrum 0.61 ± 0.08 N/A 3.30 ± 0.81 N/A pixel- 0.70 ± 0.06 14.8%4.57 ± 1.08 38.5% averaging split-spectrum 0.82 ± 0.07 34.4% 6.78 ± 0.82 105% DSNR = decorrelation signal-to-noise ratio. Statistical analysisis based on 6 eyes of 3 normal human subjects.

Utilizing the technology disclosed, visualization of both larger bloodvessels and the capillary network in the retinal and choroidalcirculations has been demonstrated. This visualization can also beenachieved using Doppler and other phase-based flow detection techniques,however the SSADA (i.e., the split-spectrum) techniques disclosed haveseveral potential advantages over phase-based techniques. Insensitivityto phase noise is one advantage. Another advantage includes the abilityto quantify microvascular flow. Because the effective resolution cell ismade isotropic (having the same size in X, Y, and Z dimensions, asdescribed in FIG. 2), it is equally sensitive to transverse (X, Y) andaxial (Z) flow. This contrasts with all phase-based techniques, whichare intrinsically more sensitive to flow in the axial direction overwhich Doppler shift occurs. Thus utilizing the technology disclosedresults in the decorrelation value as a function of the flow velocityregardless of direction. The faster blood particles move across thelaser beam, the higher the decorrelation index of the received signalswithin a velocity range set by the scan parameters. In theory thesaturation velocity should be approximately the size of the resolutioncell (0.018 mm) divided by the interframe time delay (0.002 sec), or 9mm/sec. The minimum detectable flow velocity can be determined by thedecorrelation noise floor, which can be based on the decorrelationdistribution statistics of the non-flow tissue voxels. In this example,the projection view of split-spectrum technology showed the vascularpattern within the macular capillary zone (parafoveal region). Thisdescribes that the split-spectrum technology disclosed is able to detectretinal capillary flow, which is within the range of 0.5-2 mm/sec.Calibration of velocity to decorrelation values using in vitro flowphantom experiments can be done to further determine the minimumdetectable flow velocity.

The projection of flow from proximal (shallower) layers to distal(deeper) layers can be challenging. Flow in the major peripapillaryretinal arteries and veins (as shown in FIG. 12) and larger macularvessels in the inner retina (as shown in FIG. 13) projects onto thehighly reflective RPE, which should not contain any blood vessels. Therewere also probable projection of flow from the more superficial innerretinal layers (i.e. nerve fiber layer and ganglion cell layer) to thedeeper inner retinal layers (i.e. inner and outer plexiform layers).This does not affect the accuracy of en face projection of the retinalcirculation, but it could affect the accuracy of cross-sectionalangiograms and en face projection of the choroidal circulation. One canraise the threshold decorrelation value for flow identification indeeper voxels if a more superficial voxel has a suprathresholddecorrelation value; however, this can inevitably introduce a potentialshadow artifact in place of a flow projection artifact. Thus, images ofdeeper vessels can be interpreted with this artifact in mind.

Noise from bulk tissue motion, while dramatically reduced using thetechnology disclosed herein, may not be entirely eliminated. Asdescribed in the examples disclosed, no attempt was made to compensatefor X-Z motion between consecutive B-scan frames by the use offrame-shift registration. This registration can likely reduce the effectof bulk motion in the X-Z dimensions (though not in the Y direction) andimprove the accuracy of flow detection further. It is also apparent fromthe en face angiograms that there are saccadic motion artifacts in the3D dataset. This can likely be reduced by the use of 3D registrationalgorithms.

The disclosure set forth above encompasses multiple distinctembodiments. While each of these embodiments have been disclosed in itspreferred form, the specific embodiments as disclosed and illustratedherein are not to be considered in a limiting sense as numerousvariations are possible. The subject matter of the present disclosureincludes all novel and non-obvious combinations and subcombinations ofthe various elements, features, functions and/or properties disclosedherein. Similarly, where any claim recites “a” or “a first” element orthe equivalent thereof, such claim should be understood to includeincorporation of one or more such elements, neither requiring norexcluding two or more such elements.

What is claimed is:
 1. A method of imaging, comprising: scanning aflowing sample to obtain M-B scans of OCT spectrum; splitting the M-Bscans of OCT spectrum into M spectral bands; determining a flow imagefrom the M spectral bands.
 2. The method of claim 1, wherein splittingthe M-B scans of OCT spectrum into M spectral bands comprises: creatingoverlapping filters covering the OCT spectrum; and filtering the OCTspectrum with the overlapping filters.
 3. The method of claim 1, whereindetermining a flow image from the M spectral bands comprises: creatingdecorrelation images for the M spectral bands; and combining thedecorrelation images for the M spectral bands to create a flow image. 4.The method of claim 3, wherein creating decorrelation images for Mspectral bands comprises: determining amplitude information for eachspectral band; and calculating decorrelation between adjacent amplitudeframes for each spectral band.
 5. The method of claim 4, furthercomprising removing background noise.
 6. The method of claim 3, whereincombining the decorrelation images for the M spectral bands to create aflow image comprises; averaging the decorrelation images for eachspectral band to create an average decorrelation image for each spectralband; and averaging the averaged decorrelation images from the Mspectral bands.
 7. The method of claim 6, further comprising eliminatingdecorrelation images for each spectral band having excessive motionnoise.
 8. A method of modifying an OCT imaging resolution cell from OCTspectrum to create an isotropic resolution cell, comprising: creatingoverlapping filters covering the OCT spectrum; and filtering the OCTspectrum with the overlapping filters.
 9. The method of claim 8, whereincreating overlapping filters comprises creating a filter bank comprisedof at least one specification.
 10. The method of claim 9, wherein the atleast one specification is comprised of one or more factors comprisingat least one of a filter type, a bandwidth of a filter, an overlapbetween different bands, and a number of bands.
 11. A system for in vivoimaging, comprising: an optical coherence tomography apparatus; and oneor more processors coupled to the apparatus and adapted to cause theapparatus to: obtain M-B scans of OCT spectrum from a flowing sample;split the M-B scans of OCT spectrum into M spectral bands; and determinea flow image from the M spectral bands.
 12. The system of claim 11,wherein the one or more processors adapted to cause the apparatus tosplit the M-B scans of OCT spectrum into M spectral bands furthercomprises being adapted to cause the apparatus to: create overlappingfilters covering the OCT spectrum; and filter the OCT spectrum with theoverlapping filters.
 13. The system of claim 11, wherein the one or moreprocessors adapted to cause the apparatus to determine a flow image fromthe M spectral bands further comprises being adapted to cause theapparatus to: create decorrelation images for the M spectral bands; andcombine the decorrelation images for the M spectral bands to create aflow image.
 14. The system of claim 13, wherein the one or moreprocessors adapted to cause the apparatus to create decorrelation imagesfor M spectral bands further comprises being adapted to cause theapparatus to: determine amplitude information for each spectral band;and calculate decorrelation between adjacent amplitude frames for eachspectral band.
 15. The system of claim 13, wherein the one or moreprocessors adapted to cause the apparatus to combine the decorrelationimages for the M spectral bands to create a flow image further comprisesbeing adapted to cause the apparatus to: average the decorrelationimages for each spectral band to create an average decorrelation imagefor each spectral band; and average the averaged decorrelation imagesfrom the M spectral bands.